The package implements an integrated editing and imputation for continuous microdata under linear constraints. It relies on a Bayesian nonparametric hierarchical modeling approach in which the joint distribution of the data is estimated by a flexible joint probability model. The generated edit-imputed data are guaranteed to satisfy all imposed edit rules, whose types include ratio edits, balance edits and range restrictions.
|License:||GPL (>= 3)|
Quanli Wang, Hang J. Kim, Jerome P. Reiter, Lawrence H. Cox and Alan F. Karr
Hang J. Kim, Lawrence H. Cox, Alan F. Karr, Jerome P. Reiter and Quanli Wang (2015). "Simultaneous Edit-Imputation for Continuous Microdata", Journal of the American Statistical Association, DOI: 10.1080/01621459.2015.1040881.
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library(EditImputeCont) ## read the toy example data, which has two ratio edits and a balance edit data(SimpleEx) data1 = readData(Y.original=SimpleEx$D.obs, ratio=SimpleEx$Ratio.edit, range=NULL, balance=SimpleEx$Balance.edit) ## create and initialize the model with 15 DP mixture components model1 = createModel(data.obj=data1, K=15) ## Run an iteration of MCMC # model1$Iterate() # dim(model1$Y.edited) ##  1000 4 # Edit-imputed datasets of n=1000 records with p=4 variables ## Please see the example in the demo folder for more detailed explanation
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